This notebook walks you through one of the most popular Udacity projects across machine learning and artificial intellegence nanodegree programs. The goal is to classify images of dogs according to their breed.
If you are looking for a more guided capstone project related to deep learning and convolutional neural networks, this might be just it. Notice that even if you follow the notebook to creating your classifier, you must still create a blog post or deploy an application to fulfill the requirements of the capstone project.
Also notice, you may be able to use only parts of this notebook (for example certain coding portions or the data) without completing all parts and still meet all requirements of the capstone project.
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:
train_files, valid_files, test_files - numpy arrays containing file paths to imagestrain_targets, valid_targets, test_targets - numpy arrays containing onehot-encoded classification labelsdog_names - list of string-valued dog breed names for translating labelsfrom sklearn.datasets import load_files
from keras.utils import np_utils
import numpy as np
from glob import glob
import shutil
shutil.copytree
# define function to load train, test, and validation datasets
def load_dataset(path):
data = load_files(path)
dog_files = np.array(data['filenames'])
dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
return dog_files, dog_targets
# load train, test, and validation datasets
train_files, train_targets = load_dataset('data/dog_images/train')
valid_files, valid_targets = load_dataset('data/dog_images/valid')
test_files, test_targets = load_dataset('data/dog_images/test')
# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("data/dog_images/train/*/"))]
# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
There are 133 total dog categories. There are 8351 total dog images. There are 6680 training dog images. There are 835 validation dog images. There are 836 test dog images.
In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.
import random
random.seed(8675309)
# load filenames in shuffled human dataset
human_files = np.array(glob("data/lfw/*/*"))
random.shuffle(human_files)
# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
There are 13233 total human images.
import pandas as pd
# exploratory analysis of the dog images
dog_dict = {}
for dog_name in dog_names:
dog_name = dog_name.split('.')[-1]
dog_dict[dog_name] = 0
for file in train_files:
file_dog_breed = file.split('.')[-2].split('\\')[0]
dog_dict[file_dog_breed] += 1
df_dog_breed = pd.DataFrame(list(dog_dict.values()), columns=['Count'], index=list(dog_dict.keys()))
df_dog_breed = df_dog_breed.sort_values('Count', ascending=False)
df_dog_breed.head()
| Count | |
|---|---|
| Alaskan_malamute | 77 |
| Border_collie | 74 |
| Basset_hound | 73 |
| Dalmatian | 71 |
| Basenji | 69 |
df_dog_breed.plot(kind='barh', figsize=(15, 50), legend=False, xlabel='Count', ylabel='Dog Breed')
<AxesSubplot: xlabel='Count', ylabel='Dog Breed'>
We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.
In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# print number of faces detected in the image
print('Number of faces detected:', len(faces))
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Number of faces detected: 1
Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.
In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.
We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
return len(faces) > 0
Question 1: Use the code cell below to test the performance of the face_detector function.
human_files have a detected human face?dog_files have a detected human face?Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.
Answer: 99% of the first 100 images in human_files have a detected human face. 12% of the first 100 images in dog_files have a detected human face.
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.
from PIL import Image
from tqdm import tqdm
## TODO: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
# test on human images
true_positive = 0
for file in human_files_short:
if face_detector(file):
true_positive += 1
else:
# display falsely classified human image
print("I don't see any human here?!")
plt.show(plt.imshow(Image.open(file)))
print('Number of true positives (humans identified as humans): {} of 100'.format(true_positive))
# test on dog images
false_positive = 0
for file in dog_files_short:
if face_detector(file):
false_positive += 1
# display falsely classified dog image
print("This doesn't look like a dog to me?!")
plt.show(plt.imshow(Image.open(file)))
print('Number of false positives (dogs identified as humans): {} of 100'.format(false_positive))
I don't see any human here?!
Number of true positives (humans identified as humans): 99 of 100 This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
Number of false positives (dogs identified as humans): 12 of 100
Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?
Answer: No, this expectation is not reasonable, because not all pictures of humans have a clearly presented face. Another way to detect humans is to use a object detection model that was trained on identifying different parts of the human body and not only the face. One of these models is the "Faster RCN Inception V2 COCO Model", which was used in the following optional task.
We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.
# Code adapted from https://gist.github.com/madhawav/1546a4b99c8313f06c0b2d7d7b4a09e2
import numpy as np
import tensorflow as tf
import cv2
import time
class DetectorAPI:
def __init__(self, path_to_ckpt):
self.path_to_ckpt = path_to_ckpt
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.compat.v1.GraphDef()
with tf.compat.v2.io.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.default_graph = self.detection_graph.as_default()
self.sess = tf.compat.v1.Session(graph=self.detection_graph)
# Definite input and output Tensors for detection_graph
self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
def processFrame(self, image):
# Expand dimensions since the trained_model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image, axis=0)
# Actual detection.
(boxes, scores, classes, num) = self.sess.run(
[self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],
feed_dict={self.image_tensor: image_np_expanded})
im_height, im_width,_ = image.shape
boxes_list = [None for i in range(boxes.shape[1])]
for i in range(boxes.shape[1]):
boxes_list[i] = (int(boxes[0,i,0] * im_height),
int(boxes[0,i,1]*im_width),
int(boxes[0,i,2] * im_height),
int(boxes[0,i,3]*im_width))
return boxes_list, scores[0].tolist(), [int(x) for x in classes[0].tolist()], int(num[0])
def close(self):
self.sess.close()
self.default_graph.close()
# threshold for the detection score
threshold = 0.95
# returns "True" if at least one human is detected in image stored at img_path
def human_detector(model, img_path):
img = plt.imread(img_path)
boxes, scores, classes, num = model.processFrame(img)
for i in range(len(boxes)):
# Class 1 represents human
if classes[i] == 1 and scores[i] > threshold:
return True
return False
from IPython.utils import io
import PIL.Image
# Initiate pretrained Faster RCN Inception V2 COCO Model
model_path = 'faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb'
model = DetectorAPI(path_to_ckpt=model_path)
# test on human images
true_positive = 0
for file in human_files_short:
with io.capture_output() as captured:
faces = human_detector(model, file)
if faces:
true_positive += 1
else:
# display falsely classified human image
print("I don't see any human here?!")
plt.show(plt.imshow(PIL.Image.open(file)))
print('Number of true positives (humans identified as humans): {} of 100'.format(true_positive))
# test on dog images
false_positive = 0
for file in dog_files_short:
with io.capture_output() as captured:
faces = human_detector(model, file)
if faces:
false_positive += 1
# display falsely classified dog image
print("This doesn't look like a dog to me?!")
plt.show(plt.imshow(PIL.Image.open(file)))
print('Number of false positives (dogs identified as humans): {} of 100'.format(false_positive))
Number of true positives (humans identified as humans): 100 of 100 This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
This doesn't look like a dog to me?!
Number of false positives (dogs identified as humans): 5 of 100
Answer: The "Faster RCN Inception V2 COCO Model" shows a better performance for both datasets compared to the previously used OpenCV model. 100% of the first 100 images in human_files have a detected human. 5% of the first 100 images in dog_files have a detected human. In the latter case, the misclassified images indicate that most of them have both a human and one or multiple dogs showing. This special case of both object classes present in one image could be interesting to analyze for future work.
In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.
from keras.applications.resnet import ResNet50
# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5 102967424/102967424 [==============================] - 10s 0us/step
When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape
$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.
The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape
The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape
Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!
from keras.utils import image_utils
from tqdm import tqdm
def path_to_tensor(img_path):
# loads RGB image as PIL.Image.Image type
img = image_utils.load_img(img_path, target_size=(224, 224))
# convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
x = image_utils.img_to_array(img)
# convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
return np.expand_dims(x, axis=0)
def paths_to_tensor(img_paths):
list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
return np.vstack(list_of_tensors)
Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.
Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.
By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.
from keras.applications.resnet import preprocess_input, decode_predictions
def ResNet50_predict_labels(img_path):
# returns prediction vector for image located at img_path
img = preprocess_input(path_to_tensor(img_path))
return np.argmax(ResNet50_model.predict(img))
While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).
We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
prediction = ResNet50_predict_labels(img_path)
return ((prediction <= 268) & (prediction >= 151))
Question 3: Use the code cell below to test the performance of your dog_detector function.
human_files_short have a detected dog?dog_files_short have a detected dog?Answer: 0% of the first 100 images in human_files have a detected dog. 100% of the first 100 images in dog_files have a detected dog.
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
from IPython.utils import io
# test on human images
false_positive = 0
for file in human_files_short:
with io.capture_output() as captured:
faces = dog_detector(file)
if faces:
false_positive += 1
# display falsely classified human image
print("I don't see any human here?!")
plt.show(plt.imshow(Image.open(file)))
print('Number of false positives (humans identified as dogs): {} of 100'.format(false_positive))
# test on dog images
true_positive = 0
for file in dog_files_short:
with io.capture_output() as captured:
faces = dog_detector(file)
if faces:
true_positive += 1
else:
# display falsely classified dog image
print("This doesn't look like a dog to me?!")
plt.show(plt.imshow(Image.open(file)))
print('Number of true positives (dogs identified as dogs): {} of 100'.format(true_positive))
Number of false positives (humans identified as dogs): 0 of 100 Number of true positives (dogs identified as dogs): 100 of 100
Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.
Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.
We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.
| Brittany | Welsh Springer Spaniel |
|---|---|
![]() |
![]() |
It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).
| Curly-Coated Retriever | American Water Spaniel |
|---|---|
![]() |
![]() |
Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.
| Yellow Labrador | Chocolate Labrador | |
|---|---|---|
![]() |
![]() |
![]() |
We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.
Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
We rescale the images by dividing every pixel in every image by 255.
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
100%|██████████| 6680/6680 [00:32<00:00, 206.79it/s] 100%|██████████| 835/835 [00:04<00:00, 181.72it/s] 100%|██████████| 836/836 [00:04<00:00, 207.94it/s]
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
model.summary()
We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.
Answer: I decided to use the suggested architecture as a suitable starting point since it provides a reasonable number of layers, which in turn would mean a good trade-off between training duration and number of parameters. This could help to detect meaningful patterns in the otherwise difficult to distinguish characteristics of different dog breeds.
In addition to the convolution and pooling layers, I added a batch normalization pattern, wich help to handle internal covariate shift by normalizing the hidden representations gained during training, i.e., the output of the convolution and pooling layers. Moreover, I added a dropout layer between the global average pooling layer and the dense layer to randomly switch off 20% of the neurons of the model. This step helps to enhance the learning of our model.
The three convolution layers use the Relu Activation function, whereas a Softmax activation function was applied to the final dense layer.
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
num_classes = len(dog_names)
model = Sequential()
### TODO: Define your architecture.
model.add(Conv2D(input_shape=(224, 224, 3), filters=16, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(BatchNormalization())
model.add(Conv2D(filters = 32, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(BatchNormalization())
model.add(Conv2D(filters = 64, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(BatchNormalization())
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
Model: "sequential_19"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2009 (Conv2D) (None, 223, 223, 16) 208
max_pooling2d_1029 (MaxPool (None, 111, 111, 16) 0
ing2D)
batch_normalization_12 (Bat (None, 111, 111, 16) 64
chNormalization)
conv2d_2010 (Conv2D) (None, 110, 110, 32) 2080
max_pooling2d_1030 (MaxPool (None, 55, 55, 32) 0
ing2D)
batch_normalization_13 (Bat (None, 55, 55, 32) 128
chNormalization)
conv2d_2011 (Conv2D) (None, 54, 54, 64) 8256
max_pooling2d_1031 (MaxPool (None, 27, 27, 64) 0
ing2D)
batch_normalization_14 (Bat (None, 27, 27, 64) 256
chNormalization)
global_average_pooling2d_17 (None, 64) 0
(GlobalAveragePooling2D)
dropout_14 (Dropout) (None, 64) 0
dense_1159 (Dense) (None, 133) 8645
=================================================================
Total params: 19,637
Trainable params: 19,413
Non-trainable params: 224
_________________________________________________________________
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
from keras.callbacks import ModelCheckpoint
### TODO: specify the number of epochs that you would like to use to train the model.
epochs = 5
### Do NOT modify the code below this line.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5',
verbose=1, save_best_only=True)
model.fit(train_tensors, train_targets,
validation_data=(valid_tensors, valid_targets),
epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
Epoch 1/5 334/334 [==============================] - ETA: 0s - loss: 4.8636 - accuracy: 0.0163 Epoch 1: val_loss improved from inf to 4.91515, saving model to saved_models\weights.best.from_scratch.hdf5 334/334 [==============================] - 461s 1s/step - loss: 4.8636 - accuracy: 0.0163 - val_loss: 4.9152 - val_accuracy: 0.0072 Epoch 2/5 334/334 [==============================] - ETA: 0s - loss: 4.7409 - accuracy: 0.0272 Epoch 2: val_loss improved from 4.91515 to 4.78025, saving model to saved_models\weights.best.from_scratch.hdf5 334/334 [==============================] - 454s 1s/step - loss: 4.7409 - accuracy: 0.0272 - val_loss: 4.7803 - val_accuracy: 0.0228 Epoch 3/5 334/334 [==============================] - ETA: 0s - loss: 4.6804 - accuracy: 0.0346 Epoch 3: val_loss improved from 4.78025 to 4.69954, saving model to saved_models\weights.best.from_scratch.hdf5 334/334 [==============================] - 457s 1s/step - loss: 4.6804 - accuracy: 0.0346 - val_loss: 4.6995 - val_accuracy: 0.0263 Epoch 4/5 334/334 [==============================] - ETA: 0s - loss: 4.6238 - accuracy: 0.0400 Epoch 4: val_loss improved from 4.69954 to 4.68685, saving model to saved_models\weights.best.from_scratch.hdf5 334/334 [==============================] - 431s 1s/step - loss: 4.6238 - accuracy: 0.0400 - val_loss: 4.6869 - val_accuracy: 0.0407 Epoch 5/5 334/334 [==============================] - ETA: 0s - loss: 4.5832 - accuracy: 0.0451 Epoch 5: val_loss improved from 4.68685 to 4.61825, saving model to saved_models\weights.best.from_scratch.hdf5 334/334 [==============================] - 417s 1s/step - loss: 4.5832 - accuracy: 0.0451 - val_loss: 4.6182 - val_accuracy: 0.0479
<keras.callbacks.History at 0x279e7d0e1f0>
model.load_weights('saved_models/weights.best.from_scratch.hdf5')
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.
from IPython.utils import io
# get index of predicted dog breed for each image in test set
with io.capture_output() as captured:
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]
# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 4.5455%
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']
The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))
VGG16_model.summary()
Model: "sequential_20"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
global_average_pooling2d_18 (None, 512) 0
(GlobalAveragePooling2D)
dense_1160 (Dense) (None, 133) 68229
=================================================================
Total params: 68,229
Trainable params: 68,229
Non-trainable params: 0
_________________________________________________________________
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5',
verbose=1, save_best_only=True)
VGG16_model.fit(train_VGG16, train_targets,
validation_data=(valid_VGG16, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
Epoch 1/20 327/334 [============================>.] - ETA: 0s - loss: 8.1740 - accuracy: 0.2222 Epoch 1: val_loss improved from inf to 3.64412, saving model to saved_models\weights.best.VGG16.hdf5 334/334 [==============================] - 6s 17ms/step - loss: 8.0801 - accuracy: 0.2257 - val_loss: 3.6441 - val_accuracy: 0.4515 Epoch 2/20 329/334 [============================>.] - ETA: 0s - loss: 2.2443 - accuracy: 0.5938 Epoch 2: val_loss improved from 3.64412 to 2.41903, saving model to saved_models\weights.best.VGG16.hdf5 334/334 [==============================] - 3s 9ms/step - loss: 2.2381 - accuracy: 0.5949 - val_loss: 2.4190 - val_accuracy: 0.5892 Epoch 3/20 328/334 [============================>.] - ETA: 0s - loss: 1.2763 - accuracy: 0.7338 Epoch 3: val_loss improved from 2.41903 to 2.14024, saving model to saved_models\weights.best.VGG16.hdf5 334/334 [==============================] - 3s 8ms/step - loss: 1.2725 - accuracy: 0.7349 - val_loss: 2.1402 - val_accuracy: 0.6431 Epoch 4/20 333/334 [============================>.] - ETA: 0s - loss: 0.8347 - accuracy: 0.8095 Epoch 4: val_loss improved from 2.14024 to 2.06666, saving model to saved_models\weights.best.VGG16.hdf5 334/334 [==============================] - 3s 9ms/step - loss: 0.8358 - accuracy: 0.8093 - val_loss: 2.0667 - val_accuracy: 0.6455 Epoch 5/20 332/334 [============================>.] - ETA: 0s - loss: 0.5745 - accuracy: 0.8583 Epoch 5: val_loss improved from 2.06666 to 1.96039, saving model to saved_models\weights.best.VGG16.hdf5 334/334 [==============================] - 3s 8ms/step - loss: 0.5758 - accuracy: 0.8578 - val_loss: 1.9604 - val_accuracy: 0.6802 Epoch 6/20 328/334 [============================>.] - ETA: 0s - loss: 0.4034 - accuracy: 0.8951 Epoch 6: val_loss did not improve from 1.96039 334/334 [==============================] - 3s 8ms/step - loss: 0.4044 - accuracy: 0.8946 - val_loss: 2.1490 - val_accuracy: 0.6683 Epoch 7/20 331/334 [============================>.] - ETA: 0s - loss: 0.2990 - accuracy: 0.9180 Epoch 7: val_loss did not improve from 1.96039 334/334 [==============================] - 3s 8ms/step - loss: 0.3015 - accuracy: 0.9180 - val_loss: 1.9760 - val_accuracy: 0.7006 Epoch 8/20 328/334 [============================>.] - ETA: 0s - loss: 0.2136 - accuracy: 0.9416 Epoch 8: val_loss did not improve from 1.96039 334/334 [==============================] - 3s 8ms/step - loss: 0.2158 - accuracy: 0.9412 - val_loss: 1.9912 - val_accuracy: 0.7042 Epoch 9/20 329/334 [============================>.] - ETA: 0s - loss: 0.1751 - accuracy: 0.9500 Epoch 9: val_loss improved from 1.96039 to 1.87513, saving model to saved_models\weights.best.VGG16.hdf5 334/334 [==============================] - 3s 8ms/step - loss: 0.1758 - accuracy: 0.9497 - val_loss: 1.8751 - val_accuracy: 0.7186 Epoch 10/20 331/334 [============================>.] - ETA: 0s - loss: 0.1348 - accuracy: 0.9610 Epoch 10: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.1345 - accuracy: 0.9609 - val_loss: 1.8958 - val_accuracy: 0.7138 Epoch 11/20 329/334 [============================>.] - ETA: 0s - loss: 0.1128 - accuracy: 0.9672 Epoch 11: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.1125 - accuracy: 0.9672 - val_loss: 2.0558 - val_accuracy: 0.7198 Epoch 12/20 328/334 [============================>.] - ETA: 0s - loss: 0.0756 - accuracy: 0.9776 Epoch 12: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.0764 - accuracy: 0.9772 - val_loss: 1.9874 - val_accuracy: 0.7269 Epoch 13/20 328/334 [============================>.] - ETA: 0s - loss: 0.0631 - accuracy: 0.9814 Epoch 13: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.0638 - accuracy: 0.9811 - val_loss: 1.9903 - val_accuracy: 0.7138 Epoch 14/20 332/334 [============================>.] - ETA: 0s - loss: 0.0540 - accuracy: 0.9842 Epoch 14: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.0537 - accuracy: 0.9843 - val_loss: 2.1298 - val_accuracy: 0.7138 Epoch 15/20 332/334 [============================>.] - ETA: 0s - loss: 0.0367 - accuracy: 0.9870 Epoch 15: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.0365 - accuracy: 0.9871 - val_loss: 2.1526 - val_accuracy: 0.7305 Epoch 16/20 334/334 [==============================] - ETA: 0s - loss: 0.0356 - accuracy: 0.9889 Epoch 16: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.0356 - accuracy: 0.9889 - val_loss: 2.0575 - val_accuracy: 0.7377 Epoch 17/20 332/334 [============================>.] - ETA: 0s - loss: 0.0299 - accuracy: 0.9911 Epoch 17: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.0297 - accuracy: 0.9912 - val_loss: 2.0599 - val_accuracy: 0.7341 Epoch 18/20 333/334 [============================>.] - ETA: 0s - loss: 0.0273 - accuracy: 0.9931 Epoch 18: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.0273 - accuracy: 0.9931 - val_loss: 2.1852 - val_accuracy: 0.7389 Epoch 19/20 328/334 [============================>.] - ETA: 0s - loss: 0.0241 - accuracy: 0.9933 Epoch 19: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.0242 - accuracy: 0.9933 - val_loss: 2.1975 - val_accuracy: 0.7246 Epoch 20/20 331/334 [============================>.] - ETA: 0s - loss: 0.0234 - accuracy: 0.9938 Epoch 20: val_loss did not improve from 1.87513 334/334 [==============================] - 3s 8ms/step - loss: 0.0232 - accuracy: 0.9939 - val_loss: 2.1076 - val_accuracy: 0.7473
<keras.callbacks.History at 0x279e9656d60>
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')
Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.
from IPython.utils import io
# get index of predicted dog breed for each image in test set
with io.capture_output() as captured:
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]
# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 73.4450%
from extract_bottleneck_features import *
def VGG16_predict_breed(img_path):
# extract bottleneck features
bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
# obtain predicted vector
predicted_vector = VGG16_model.predict(bottleneck_feature)
# return dog breed that is predicted by the model
return dog_names[np.argmax(predicted_vector)]
You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.
In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:
The files are encoded as such:
Dog{network}Data.npz
where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.
In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:
bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
### TODO: Obtain bottleneck features from another pre-trained CNN.
bottleneck_features = np.load('bottleneck_features/DogInceptionV3Data.npz')
train_inceptionV3 = bottleneck_features['train']
valid_inceptionV3 = bottleneck_features['valid']
test_inceptionV3 = bottleneck_features['test']
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
<your model's name>.summary()
Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
Answer: The following image shows the Inception V3 architecture [doi: 10.1109/SIBGRAPI.2018.00012]

In order to adapt this network to our dog breed classification use case, we need to add a global average pooling layer that uses the input shape of our training data and a final dense layer that uses the number of to be classified dog breeds as its output shape.
### TODO: Define your architecture.
inceptionV3_model = Sequential()
inceptionV3_model.add(GlobalAveragePooling2D(input_shape=train_inceptionV3.shape[1:]))
inceptionV3_model.add(Dense(133, activation='softmax'))
inceptionV3_model.summary()
Model: "sequential_21"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
global_average_pooling2d_19 (None, 2048) 0
(GlobalAveragePooling2D)
dense_1161 (Dense) (None, 133) 272517
=================================================================
Total params: 272,517
Trainable params: 272,517
Non-trainable params: 0
_________________________________________________________________
### TODO: Compile the model.
inceptionV3_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
### TODO: Train the model.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.inceptionv3.hdf5',
verbose=1,
save_best_only=True
)
inceptionV3_model.fit(train_inceptionV3, train_targets,
validation_data=(valid_inceptionV3, valid_targets),
epochs=20,
batch_size=20,
callbacks=[checkpointer],
verbose=1)
Epoch 1/20 334/334 [==============================] - ETA: 0s - loss: 0.5048 - accuracy: 0.8501 Epoch 1: val_loss improved from inf to 0.65294, saving model to saved_models\weights.best.inceptionv3.hdf5 334/334 [==============================] - 4s 11ms/step - loss: 0.5048 - accuracy: 0.8501 - val_loss: 0.6529 - val_accuracy: 0.8443 Epoch 2/20 333/334 [============================>.] - ETA: 0s - loss: 0.3804 - accuracy: 0.8857 Epoch 2: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 11ms/step - loss: 0.3800 - accuracy: 0.8859 - val_loss: 0.6836 - val_accuracy: 0.8228 Epoch 3/20 330/334 [============================>.] - ETA: 0s - loss: 0.3038 - accuracy: 0.9058 Epoch 3: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 11ms/step - loss: 0.3028 - accuracy: 0.9060 - val_loss: 0.6673 - val_accuracy: 0.8443 Epoch 4/20 333/334 [============================>.] - ETA: 0s - loss: 0.2441 - accuracy: 0.9234 Epoch 4: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 11ms/step - loss: 0.2435 - accuracy: 0.9237 - val_loss: 0.6855 - val_accuracy: 0.8431 Epoch 5/20 334/334 [==============================] - ETA: 0s - loss: 0.2103 - accuracy: 0.9352 Epoch 5: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 11ms/step - loss: 0.2103 - accuracy: 0.9352 - val_loss: 0.7262 - val_accuracy: 0.8419 Epoch 6/20 331/334 [============================>.] - ETA: 0s - loss: 0.1733 - accuracy: 0.9450 Epoch 6: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 11ms/step - loss: 0.1726 - accuracy: 0.9449 - val_loss: 0.7402 - val_accuracy: 0.8467 Epoch 7/20 331/334 [============================>.] - ETA: 0s - loss: 0.1474 - accuracy: 0.9535 Epoch 7: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 11ms/step - loss: 0.1491 - accuracy: 0.9530 - val_loss: 0.7771 - val_accuracy: 0.8503 Epoch 8/20 330/334 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.9591 Epoch 8: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 11ms/step - loss: 0.1250 - accuracy: 0.9588 - val_loss: 0.8855 - val_accuracy: 0.8395 Epoch 9/20 334/334 [==============================] - ETA: 0s - loss: 0.1043 - accuracy: 0.9662 Epoch 9: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 11ms/step - loss: 0.1043 - accuracy: 0.9662 - val_loss: 0.7941 - val_accuracy: 0.8515 Epoch 10/20 329/334 [============================>.] - ETA: 0s - loss: 0.0899 - accuracy: 0.9717 Epoch 10: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 11ms/step - loss: 0.0922 - accuracy: 0.9714 - val_loss: 0.8440 - val_accuracy: 0.8551 Epoch 11/20 333/334 [============================>.] - ETA: 0s - loss: 0.0792 - accuracy: 0.9733 Epoch 11: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 12ms/step - loss: 0.0790 - accuracy: 0.9734 - val_loss: 0.7868 - val_accuracy: 0.8623 Epoch 12/20 330/334 [============================>.] - ETA: 0s - loss: 0.0680 - accuracy: 0.9786 Epoch 12: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 12ms/step - loss: 0.0679 - accuracy: 0.9786 - val_loss: 0.8867 - val_accuracy: 0.8647 Epoch 13/20 332/334 [============================>.] - ETA: 0s - loss: 0.0639 - accuracy: 0.9797 Epoch 13: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 12ms/step - loss: 0.0642 - accuracy: 0.9796 - val_loss: 0.9427 - val_accuracy: 0.8539 Epoch 14/20 330/334 [============================>.] - ETA: 0s - loss: 0.0549 - accuracy: 0.9839 Epoch 14: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 12ms/step - loss: 0.0548 - accuracy: 0.9838 - val_loss: 0.9094 - val_accuracy: 0.8551 Epoch 15/20 331/334 [============================>.] - ETA: 0s - loss: 0.0464 - accuracy: 0.9866 Epoch 15: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 13ms/step - loss: 0.0460 - accuracy: 0.9867 - val_loss: 0.9390 - val_accuracy: 0.8467 Epoch 16/20 334/334 [==============================] - ETA: 0s - loss: 0.0431 - accuracy: 0.9868 Epoch 16: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 13ms/step - loss: 0.0431 - accuracy: 0.9868 - val_loss: 0.9936 - val_accuracy: 0.8419 Epoch 17/20 334/334 [==============================] - ETA: 0s - loss: 0.0372 - accuracy: 0.9888 Epoch 17: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 12ms/step - loss: 0.0372 - accuracy: 0.9888 - val_loss: 0.9926 - val_accuracy: 0.8503 Epoch 18/20 330/334 [============================>.] - ETA: 0s - loss: 0.0359 - accuracy: 0.9898 Epoch 18: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 13ms/step - loss: 0.0358 - accuracy: 0.9897 - val_loss: 0.9642 - val_accuracy: 0.8395 Epoch 19/20 334/334 [==============================] - ETA: 0s - loss: 0.0289 - accuracy: 0.9912 Epoch 19: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 13ms/step - loss: 0.0289 - accuracy: 0.9912 - val_loss: 0.9913 - val_accuracy: 0.8611 Epoch 20/20 330/334 [============================>.] - ETA: 0s - loss: 0.0301 - accuracy: 0.9897 Epoch 20: val_loss did not improve from 0.65294 334/334 [==============================] - 4s 12ms/step - loss: 0.0297 - accuracy: 0.9898 - val_loss: 1.0155 - val_accuracy: 0.8515
<keras.callbacks.History at 0x279e2af0220>
### TODO: Load the model weights with the best validation loss.
inceptionV3_model.load_weights('saved_models/weights.best.inceptionv3.hdf5')
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.
from IPython.utils import io
### TODO: Calculate classification accuracy on the test dataset.
with io.capture_output() as captured:
inceptionV3_predictions = [np.argmax(inceptionV3_model.predict(np.expand_dims(feature, axis=0))) for feature in test_inceptionV3]
## report test accuracy
test_accuracy = 100*np.sum(np.array(inceptionV3_predictions)==np.argmax(test_targets, axis=1))/len(inceptionV3_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 79.6651%
Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.
Similar to the analogous function in Step 5, your function should have three steps:
dog_names array defined in Step 0 of this notebook to return the corresponding breed.The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function
extract_{network}
where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
from IPython.utils import io
def predict_dog_breed(img_path):
# extract bottleneck features
bottleneck_feature = extract_InceptionV3(path_to_tensor(img_path))
# get predicted vector
with io.capture_output() as captured:
pred_vector = inceptionV3_model.predict(bottleneck_feature)
# return predicted dog breed
return dog_names[np.argmax(pred_vector)]
Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,
You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.
A sample image and output for our algorithm is provided below, but feel free to design your own user experience!

This photo looks like an Afghan Hound.
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
from IPython.display import Image, display
from IPython.utils import io
# Initiate pretrained Faster RCN Inception V2 COCO Model
model_path = 'faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb'
model = DetectorAPI(path_to_ckpt=model_path)
def breed_classification(img_path):
# plot the image to be classified
display(Image(img_path, width=200, height=200))
# get detections for both humans and dogs
with io.capture_output() as captured:
dog_detected = dog_detector(img_path)
human_detected = human_detector(model, img_path)
# predict if it is a dog
if dog_detected == 1:
print("Hello doggo, you seem to be a: ")
with io.capture_output() as captured:
predicted_breed = predict_dog_breed(img_path).partition('.')[-1]
return predicted_breed
# predict if it is a human
elif human_detected == 1:
print("Hello human being, you look a little bit like a: ")
with io.capture_output() as captured:
predicted_breed = predict_dog_breed(img_path).partition('.')[-1]
return predicted_breed
else:
return print("No dogs or humans detected.")
In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?
Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.
Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
Answer: The output is better than I expected. Most of the sample images were classified correctly, both in terms of distinguishing between dogs and humans and also regarding identifying the correct dog breed. However, for our family dog Manfred (golden retriever) and one of the Labrador retrievers wrong predictions were made, where the erroneous prediction comes from a class with very low inter-class variations compared to the true breed (at least after a brief visual inspection).
Possible improvements:
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
print(breed_classification("images\American_water_spaniel_00648.jpg"))
Hello doggo, you seem to be a: American_water_spaniel
print(breed_classification("images\Brittany_02625.jpg"))
Hello doggo, you seem to be a: Brittany
print(breed_classification("images\Curly-coated_retriever_03896.jpg"))
Hello doggo, you seem to be a: Curly-coated_retriever
print(breed_classification("images\golden_retriever_1.jpg"))
Hello doggo, you seem to be a: Golden_retriever
Now some images of our family dog ;-)
print(breed_classification("images\golden_retriever_2.jpg"))
Hello doggo, you seem to be a: Golden_retriever
print(breed_classification("images\golden_retriever_3.jpg"))
Hello doggo, you seem to be a: Kuvasz
print(breed_classification("images\golden_retriever_4.jpg"))
Hello doggo, you seem to be a: Golden_retriever
print(breed_classification("images\golden_retriever_5.jpg"))
Hello doggo, you seem to be a: Golden_retriever
print(breed_classification("images\golden_retriever_6.jpg"))
Hello doggo, you seem to be a: Kuvasz
print(breed_classification("images\Labrador_retriever_06449.jpg"))
Hello doggo, you seem to be a: Labrador_retriever
print(breed_classification("images\Labrador_retriever_06455.jpg"))
Hello doggo, you seem to be a: Chesapeake_bay_retriever
print(breed_classification("images\Labrador_retriever_06457.jpg"))
Hello doggo, you seem to be a: Labrador_retriever
print(breed_classification("images\sample_human_1.jpg"))
Hello human being, you look a little bit like a: Chesapeake_bay_retriever
print(breed_classification("images\sample_human_2.jpg"))
Hello human being, you look a little bit like a: Bull_terrier
print(breed_classification("images\sample_human_3.jpg"))
Hello human being, you look a little bit like a: Finnish_spitz
print(breed_classification("images\Welsh_springer_spaniel_08203.jpg"))
Hello doggo, you seem to be a: Welsh_springer_spaniel